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Found 5 papers, 1 papers with code
Training data reduction for multilingual Spoken Language Understanding systems
Fine-tuning self-supervised pre-trained language models such as BERT has significantly improved state-of-the-art performance on natural language processing tasks.
Multi-task pre-finetuning for zero-shot cross lingual transfer
To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly tested on the resource poor language.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Metamorphic Testing of a Deep Learning based Forecaster
We focus on two statistical / machine learning based components - a) detection of co-relation between system characteristics and b) estimating the future value of a system characteristic using an LSTM (a deep learning architecture).
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing
We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers.
